Titlebook: Simulation-Based Optimization; Parametric Optimizat Abhijit Gosavi Book Nov 20101st edition Springer-Verlag US 2003 Response surface method

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Control Optimization with Learning Automata,s (SMDPs). The methodology that we will discuss in this chapter is generally referred to as Learning Automata. We have already discussed the theory of learning automata in the context of parametric optimization. It turns out that in control optimization too, in particular for solving problems modele

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Convergence Analysis of Parametric Optimization Methods,g “convergence analysis” of a method is to identify (mathematically) the solution to which the method converges. Hence to prove that an algorithm works, one must show that the algorithm converges to the optimal solution. In this chapter, this is precisely what we will attempt to do with some algorit

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Case Studies,chastic optimization problems. We will provide a general description of the problem and of the approach used in the solution process, rather than describing numerical details. Some of the problems discussed here can be solved both by control optimization and by parametric optimization methods.

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Book Nov 20101st editionzation. ..The book‘s objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these

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Convergence: Background Material, may skip this chapter. To follow Chapter 12, the reader should read all material up to and including Theorem 11.2 in this chapter. All the ideas developed in this chapter will be needed in Chapter 13.